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Whether converting from Oracle Forms or migrating from Microsoft Access, Oracle Application Express Application Migration is an effective tool to start your conversion project. This section outlines the tool’s capabilities and provides important information that should be reviewed before starting a conversion project.
It is important to separate business logic from logic for manipulating the user interface. Given the differences in user interface implementation, the majority of the associated logic from the original application will no longer be relevant. Oracle Application Express uses processes, computations, and validations to implement business logic. Some logic can also be replicated by utilizing default and source values within the Oracle Application Express item definition. Oracle Forms often uses the same screen for both querying records and then updating a single record at a time. This is achieved by performing a query directly on the form used for updating a single record.
Independent component analysis separates a multivariate signal into additive subcomponents that are maximally independent. It is implemented in scikit-learn using the Fast ICAalgorithm.
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Oracle Application Express does not support the notion of Enter Query and Execute Query. Therefore, the conversion project generates an interactive report where records can be queried with a link to a form for editing a single record. For example, a common layout in Oracle Forms, such as a master detail form, can be redeployed using a master form with reports for each of the detail views that link to separate pages for editing these details. These same detail pages can also be accessed from elsewhere in the application very easily. Oracle Forms and Microsoft Access use pessimistic locking whereby the record is locked when a user requests a record for update. This lock is maintained until the record is completed or canceled . Understanding some of the key differences between Oracle Application Express and Oracle Forms or Microsoft Access will greatly assist you in redeveloping your applications.
Typically, ICA is not used for reducing dimensionality but for separating superimposed signals. Since the ICA model does not include a noise term, for the model to be correct, whitening must be applied. This can be done internally using the whiten argument or manually using one of the PCA variants.
The PCA algorithm can be used to linearly transform the data while both reducing the dimensionality and preserve most of the explained variance at the same time. As in PCA, IncrementalPCA centers but does not scale the input data for each feature before applying the SVD. Browse the code, check out the SVN repository, or subscribe to the development log by RSS. The Redux Library for Gutenberg plugin is built with ease and performance in mind. Its module architecture and the clean code keep it extremely fast.
Unlike PCA, the representation of a vector is obtained in an additive fashion, by superimposing the components, without subtracting. Such additive models are efficient for representing images and text.
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However, with Microsoft Access the data structures and data must first be migrated across to Oracle using SQL Developer. For more information on how to migrate data structures "Migrating a Microsoft Access Application". One of the major facets of converting from either Oracle Forms or Microsoft Access is replicating the business user interface specific logic.
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attribute determines the initialization method applied, which has a great impact on the performance of the method. NMF implements the method Nonnegative Double Singular Value Decomposition. NNDSVD 4 is based on two SVD processes, one approximating the data matrix, the other approximating positive sections of the resulting partial SVD factors utilizing an algebraic property of unit rank matrices. The basic NNDSVD algorithm is better fit for sparse factorization. Its variants NNDSVDa , and NNDSVDar are recommended in the dense case.
Representing data as sparse combinations of atoms from an overcomplete dictionary is suggested to be the way the mammalian primary visual cortex works. Sparse principal components yields a more parsimonious, interpretable representation, clearly emphasizing which of the original features contribute to the differences between samples. In many cases, the real underlying components can be more naturally imagined as sparse vectors; for example in face recognition, components might naturally map to parts of faces. For instance, if we work with 64×64 pixel gray-level pictures for face recognition, the dimensionality of the data is 4096 and it is slow to train an RBF support vector machine on such wide data. Furthermore we know that the intrinsic dimensionality of the data is much lower than 4096 since all pictures of human faces look somewhat alike.
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